Idea: Metastability in complex systems

Yesterday, a few of us discussed a possible taxonomy for the lifecycle of a complex system in regard to crisis, that could perhaps give some useful structure for how we view responses to the current financial challenges. Thanks to Gareth, we’re playing with a tri-partite concept:

Systems are “stable” – there are “balancing loops” in operation, or a crisis has occurred recently and so built-up contributing factors to instability have been spent

Systems are “metastable” – optimization or other factors have built up and crossed an arbitrary threshold for crisis

Systems are “unstable” or “in crisis” – bad stuff is happening thanks to a trigger that has set off a period of instability

The idea is that we can think of appropriate responses that fit with the different parts of the cycle, thus giving a framework for anti-crisis measures. Stability should be preserved as long as possible by preventing “over-optimization” to the current environment. During periods of metastability you need more insurance, perhaps trigger small crises early to release pressure and build up contingency resources/redundancy to hedge against a large crisis. During instability you need to prevent contagion with other parts of the system and act to rebuild in a more sustainable way, using the crisis as an opportunity to learn.

The problem with this taxonomy is that perhaps complex systems are inherently metastable at all times. And the core issue is that, unlike with say avalanche risk, we don’t have a single source of pressure leading to metastability, we cannot fully understand the system, and I believe the system actually is geared to reward and hide metastability.

So while the tripartite model might be a good way at classifying responses, it shouldn’t lead us into thinking that we can know where the sources of metastability are, which phase we’re in in relation to known or unknown risks, or that we can successfully mitigate them. The illusion of control is a pernicious thing!

metastability is an important concept. Most systems evolve to stability much like an ecology. There is some interesting work on how forests and prairies evolve species regimes over time to become stable “robust” ecosystems that you may find interesting.

banking and finance regimes are metastable but become brittle with leverage or unified risk/horizon frameworks. Basel 2 implementation could be the biggest risk ever, due to its use of self similar mean variance models and the endemic responses and potential systemwide “harmonics” that will build up in the system as it matures.

There are significant risks in any system where all time and response horizons are shared. Hetrogenous non-linked components make for a more stable system than monolithic tightly coupled components.